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On-the-Fly Output Compression for Join-Based Graph Mining Algorithms

IEEE ACCESS 2018年 6卷 64008-64022页

作者： Rasel, Mostofa Kamal Lee, Young-Koo Kyung Hee Univ Dept Comp Sci & Engn Seoul South Korea

Many join-based graph mining (JGM) algorithms, such as triangle listing and clique enumeration, typically output data of such a large size that it often dominates the mining cost. Despite the significance of the I/O cost, only a few research focused on reducing the size of the output data. However, those techniques have limitation that they are highly specific to their corresponding graph mining algorithms. In this paper, through careful observations of output patterns, we propose a general compression solution that can be applied to an arbitrary JGM algorithm. The proposed technique first categorizes the nodes of a set of cliques into two lists, which contain overlapping and non-overlapping nodes, respectively. Then, we remove the redundant nodes from multiple sets of cliques by creating a Clique Constructor Tree using the overlapping and non-overlapping nodes. Finally, we compress the clique constructor trees by applying a novel flag-aligned word encoding technique. Our proposed technique performs compression on-the-fly and can easily be adopted by various JGM algorithms. Experiments on real datasets show that our proposed technique, which is adopted in a triangle listing algorithm, reduces the size of the output data and running time by seven times and about four times, respectively. Experimental results also show that a maximal clique listing algorithm can reduce the size of the output data by a factor of three.

关键词： Graph mining data compression encoding scheme on-the-fly-compression

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A new efficiently encoded multiobjective algorithm for the solution of the cardinality constrained portfolio optimization problem

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ANNALS OF OPERATIONS RESEARCH 2018年第1-2期267卷 281-319页

作者： Liagkouras, K. Metaxiotis, K. Univ Piraeus Dept Informat Decis Support Syst Lab 80 Karaoli & Dimitriou Str Piraeus 18534 Greece

This paper proposes a novel multiobjective evolutionary Algorithm (MOEA) for the solution of the cardinality constrained portfolio optimization problem (CCPOP). The proposed algorithm introduces an efficient encoding scheme specially designed for dealing with the difficulties of the CCPOP. Also, the proposed algorithm incorporates a new mutation and recombination operator tailor-made to work well with the new encoding scheme. Datasets from seven different stock markets are utilized for testing the efficiency of the proposed approach. In particular, the performance of the proposed efficiently encoded multiobjective portfolio optimization solver (EEMPOS) is assessed in comparison with two well-known MOEAs, namely NSGAII and MOEA/D. The experimental results indicate that the proposed EEMPOS outperforms the two other MOEAs for all examined performance metrics when is applied to the solution of the CCPOP for a fraction of time required by the other techniques.

关键词： Multiobjective optimization Evolutionary algorithms encoding scheme Portfolio optimization cardinality constrained

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New intelligent optimization framework

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AUTOMATIKA 2018年第2期59卷 231-253页

作者： Xie, As Zhejiang Univ Technol China Inst SMEs Hangzhou Zhejiang Peoples R China

Generally, the "intelligence" of the intelligent optimization algorithms is mainly dependent on the probability and operational rules. Thus there are always some probability equations or mathematical formulations that need to be updated. This paper proposes an algorithm model that needs no probability tuning. The algorithm designed according to the guiding principles and specific methods of benchmarking proposed in this paper is able to achieve the synergistic coexistence and automatic balance of exploration and exploitation, thus the population diversity will be kept during the running. The algorithm model proposed here is between engineering technology and cognitive philosophy, it is not just a specific algorithm, but a kind of general methodology and/or a mode of thinking. The successful application of some realistic issues, like distributed power generation optimization configuration, verified its applicability.

关键词： Intelligent optimization optimization algorithm meta-heuristic method encoding scheme benchmarking philosophy

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Improved Short Length LT Codes for BIAWGN Channels 18

Improved Short Length LT Codes for BIAWGN Channels

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18th IEEE International Conference on Communication Technology (IEEE ICCT)

作者： Song, Xin Liao, Yurong Ding, Dan Space & Engn Univ Dept Grad Management Beijing Peoples R China Space & Engn Univ Dept Elect & Opt Engn Beijing Peoples R China

ISBN: (纸本)9781538676356

While gaining increasing popularity, rateless codes, especially for short length Luby transform (LT) codes, are known to perform poorly over noisy channels. The decoding process of LT codes is analyzed, which shows that the low decoding success probability of short length LT codes is predominantly caused by lack of degree 1 check nodes (CNs). Based on this observation, a novel improved encoding scheme for short length LT codes over binary additive white gaussian noise (BIAWGN) channels is proposed. A new degree distribution and two weighting factors are presented, which are used to improve the proportion of degree 1 CNs. In addition, by setting a threshold, information nodes (INs) whose degrees are less than the threshold are screened continuously, and then extra degrees were added to these INs to improve the error floor performance of short length LT codes. Furthermore, the design principles of above parameters are presented, enabling the validity and universality of the improved scheme. Simulation results show that the improved short length LT code (k = 50 and 200) can decrease the bit error rate (BER) by up to five orders of magnitude over BIAWGN channels.

关键词： LT codes BIAWGN channels threshold encoding scheme

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Application of Constrained Multi-objective Evolutionary Algorithm in a Compressed-air Station Scheduling Problem

Application of Constrained Multi-objective Evolutionary Algo...

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第三十八届中国控制会议

作者： Jianchang liu Yongkuan Yang Shubin Tan Honghai Wang College of Information Science and Engineering Northeastern University

The compressor station is the utility that generates the compressed-air. This paper aims to present the formulation of the operation scheduling optimization problem for a compressor station, and apply a constrained multi-objective evolutionary algorithm, named CMOEA/D-CDP, to solve the problem. For this problem, it can be defined as one of the nonlinear 0-1 mixed integer multi-objective optimization problems due to the decision variables including continuous variables and 0-1 integer variables. In light of the relation between the unit commitment and rotational speed of a compressor station, a special encoding scheme which decreases the dimension of decision variables is proposed to change this nonlinear 0-1 mixed integer multi-objective optimization problem into a nonlinear continuous multi-objective optimization problem. The result obtained by CMOEA/D-CDP is a set of Pareto optimal solutions, which can help a decision maker select a specific preferred solution to guide the production under the different limits of objectives. Experimental results on two typical compressor stations indicate that the proposed encoding scheme shows apparent superiority compared to the normal encoding method.

关键词： Compressed-air station CMOEA/D-CDP Constrained multi-objective optimization encoding scheme Optimal scheduling of compressor station

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Reaching optimized parameter set: protein secondary structure prediction using neural network

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NEURAL COMPUTING & APPLICATIONS 2017年第8期28卷 1947-1974页

作者： Dongardive, Jyotshna Abraham, Siby Univ Mumbai Dept Comp Sci Bombay Maharashtra India Univ Mumbai Dept Math & Stat GN Khalsa Coll Bombay Maharashtra India

We propose an optimized parameter set for protein secondary structure prediction using three-layer feed forward back propagation neural network. The methodology uses four parameters viz. encoding scheme, window size, number of neurons in the hidden layer and type of learning algorithm. The input layer of the network consists of neurons changing from 3 to 19, corresponding to different window sizes. The hidden layer chooses a natural number from 1 to 20 as the number of neurons. The output layer consists of three neurons, each corresponding to known secondary structural classes viz. alpha-helix, beta-strands and coil/turns, respectively. It also uses eight different learning algorithms and nine encoding schemes. Exhaustive experiments were performed using non-homologous dataset. The experimental results were compared using performance measures like Q(3), sensitivity, specificity, Mathew correlation coefficient and accuracy. The paper also discusses the process of obtaining a stabilized cluster of 2530 records from a collection of 11,340 records. The graphs of these stabilized clusters of records with respect to accuracy are concave, convergence is monotonic increasing and rate of convergence is uniform. The paper gives BLOSUM62 as the encoding scheme, 19 as the window size, 19 as the number of neurons in the hidden layer and one-step secant as the learning algorithm with the highest accuracy of 78 %. These parameter values are proposed as the optimized parameter set for the three-layer feed forward back propagation neural network for the protein secondary structure prediction.

关键词： Multi-layer feed forward network Learning algorithm Proteins Hidden neuron encoding scheme Performance measures Secondary structure prediction

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Design of a Dynamic Electricity Trade Scheduler Based on Genetic Algorithms 2nd

Design of a Dynamic Electricity Trade Scheduler Based on Gen...

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2nd International Conference on Data Mining and Big Data (DMBD)

作者： Lee, Junghoon Park, Gyung-Leen Jeju Natl Univ Dept Comp Sci & Stat Jeju City South Korea

ISBN: (纸本)9783319618456;9783319618449

This paper presents a design and measures the performance of a dynamic electricity trade scheduler employing genetic algorithms for the convenient application of vehicle-to-grid services. Arriving at and being plugged-in to a microgrid, each electric vehicle specifies its stay time and sales amount, while the scheduler, invoked before each time slot, creates a connection schedule considering the microgrid-side demand and available electricity from vehicles for the given scheduling window. For the application of genetic operations, each schedule is encoded to an integer-valued vector with the complementary definition of C-space, which orderly lists all combinatory allocation maps for a task. Then, each integer element indexes a map entry in its C-space. The performance measurement result, obtained from a prototype implementation, reveals that our scheduler can stably work even when the number of sellers exceeds 100 as well as improves demand meet ratio by up to 6.3% compared with the conventional scheduler for the given parameter set.

关键词： Vehicle-to-grid Dynamic schedule Genetic algorithm encoding scheme Demand meet ratio

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Deep encoding Features for Instance Retrieval 24th

Deep Encoding Features for Instance Retrieval

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24th International Conference on Neural Information Processing (ICONIP)

作者： Ding, Zhiming Zhou, Zhengzhong Zhang, Liqing Shanghai Jiao Tong Univ Dept Comp Sci & Engn Key Lab Shanghai Educ Commiss Intelligent Interac Shanghai Peoples R China

ISBN: (纸本)9783319700908;9783319700892

In this paper, we propose a novel approach for instance retrieval. Compared with traditional retrieval pipeline, we first locate several candidate regions of target object with a region proposal network (RPN), instead of exhausting sliding window method. The candidate regions are detected through the trained RPN. Then we obtain the region-wise convolutional feature maps (CFMs) by forwarding them through a ROI pooling layer. Our feature encoding representation builds on the common sense that similar patterns have similar activations on feature maps. The target object is regarded as a combination of several meaningful patterns. In this way, we represent an image with the combination of encoded descriptors corresponding to the subsets of the proposed region. We also implement reranking algorithm to refine the proposed region in local retrieval. Through extensive experiments, we demonstrate the suitability of our feature encoding representation for instance retrieval, achieving comparable performance on both Oxford and Paris buildings benchmarks.

关键词： Deep learning encoding scheme Instance retrieval

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SVM and SVR-based MHC-binding prediction using a mathematical presentation of peptide sequences

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COMPUTATIONAL BIOLOGY AND CHEMISTRY 2016年 65卷 117-127页

作者： Jandrlic, Davorka R. Univ Belgrade Fac Mech Engn Kraljice Marije 16 Belgrade Serbia

At present, there are a number of methods for the prediction of T-cell epitopes and major histocompatibility complex (MHC)-binding peptides. Despite numerous methods for predicting T-cell epitopes, there still exist limitations that affect the reliability of prevailing methods. For this reason, the development of models with high accuracy are crucial. An accurate prediction of the peptides that bind to specific major histocompatibility complex class I and II (MHC-I and MHC-II) molecules is important for an understanding of the functioning of the immune system and the development of peptide-based vaccines. Peptide binding is the most selective step in identifying T-cell epitopes. In this paper, we present a new approach to predicting MHC-binding ligands that takes into account new weighting schemes for position-based amino acid frequencies, BLOSUM and VOGG substitution of amino acids, and the physicochemical and molecular properties of amino acids. We have made models for quantitatively and qualitatively predicting MHC-binding ligands. Our models are based on two machine learning methods support vector machine (SVM) and support vector regression (SVR), where our models have used for feature selection, several different encoding and weighting schemes for peptides. The resulting models showed comparable, and in some cases better, performance than the best existing predictors. The obtained results indicate that the physicochemical and molecular properties of amino acids (AA) contribute significantly to the peptide-binding affinity. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： T-cell epitope MHC I binding prediction Data mining Support vector machine encoding scheme

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Inspirations for Optimization based on Benchmarking

Inspirations for Optimization based on Benchmarking

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2017 International Seminar on Artificial Intelligence, Networking and Information Technology

作者： A.S.XIE D.X.LIU China Institute for SMEs Zhejiang University of Technology

The modern intelligent optimization/search algorithms were broadly classified,and some brief analysis and comment were *** we revealed some optimization rules implied in the benchmarking philosophy,which provided good... 详细信息

关键词： Optimization Problems Intelligent Optimization Optimization Algorithm Evolutionary Computation Swarm Intelligence encoding scheme Benchmarking Philosophy

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